The present invention generally relates to clinical information systems. More specifically, the present invention relates to systems and methods for clinical data validation.
Healthcare practitioners work with many forms of clinical data, such as images and measurement data from various scanners and modalities and electronic medical records (EMR). The clinical data may be stored in a number of formats. These formats may include a variety of file formats such as Digital Imaging and Communications in Medicine (DICOM) images, Joint Photographic Experts Group (JPEG) images, WAV audio files, and Portable Document Format (PDF) files; Extensible Markup Language (XML) data; and database tables, for example.
Generally, metadata is associated with core clinical data such as an image. The metadata may include, for example, patient identification, exam (accession) number, and time and place of scan. In some cases, metadata is stored as part of the same file as the core clinical data. For example, the DICOM format supports storing metadata in a clinical data file with the image information. However, in many formats, metadata is stored separately from the clinical data itself. For example, a non-DICOM image, such as a JPEG image may have metadata stored in a database. As another example, a WAV audio file containing a dictated report may have metadata stored in a database or XML file. This can lead to the core clinical data and the metadata becoming separated. As a result, there are risks ranging from the loss of the ability to use the clinical data to misidentification of data and ensuing misdiagnosis.
In addition to these risks, there is no standard mechanism for storing or validating the original source, or even the namespace, for any given piece of metadata. For example, different applications and/or systems may use different Medical Record Numbers (MRNs) to identify the same patient. An MRN may be specific to a particular system or care provider, for example.
As mentioned, medical professionals work with many kinds of clinical data. Often, a healthcare provider may view a variety of clinical data for a patient or an exam at one time. For example, a physician may review images acquired by an imaging modality along side a patient's EMR. Some applications make an effort to ensure that they only show consistent data at any one time. That is, an application may attempt to verify that the data displayed to a user is all associated with the current patient or exam. However, this requires explicit coding in each application and is error-prone.
In addition to consistency problems within a single application, consistency across multiple applications is even more problematic. Often, applications that display clinical data are provided by different vendors and thus must cooperate to try to ensure consistency.
Health Level 7 (HL7) provides a standard called Clinical Context Object Workgroup (CCOW). The CCOW standard provides for an architecture that attempts to ensure consistency across different data elements. A CCOW-compliant application communicates with a context manager to set a patient context. For example, the CCOW-compliant application may identify the MRN of the current patient. The context manager then notifies other CCOW-compliant applications of the current context. These applications, in turn, should update their internal state and display data accordingly. However, CCOW does not guarantee that the applications have done so. In addition, CCOW does not address the problem of a single application that has a bug that results in two or more pieces of inconsistent clinical data being presented to a user. Thus, while CCOW facilitates communication between components which display clinical data of the current patient context, CCOW does not validate that displayed clinical data actually complies with the patient context.
The possibility of inconsistency in the presentation of clinical data may cause inefficiencies in development and in clinical workflow, as well as clinical hazards to patients who could be misdiagnosed if an application provides inconsistent clinical data.
Thus, there exists a need for systems and methods for clinical data validation.